Engineering Manager - Inference Performance

US Health Partners, LLC; New York, Northern (NY, KY); Hybrid; USD 170,000 - 250,000; Full time

Benefits offered by this job

Competitive compensation

Equity

Medical/Dental/Vision insurance

Flexible PTO with Winter Break

Paid parental leave

Fertility and family-building stipend

401(k)

Learning opportunities

Job summary

Baseten is seeking an Engineering Manager to lead part of the Inference Performance team. You will mentor a group of inference performance engineers and drive GPU-focused optimization across kernels, scheduling, and KV-cache management. The role blends technical depth with people leadership to accelerate model workloads on GPUs.

You will hire, develop and support engineers, set direction, review designs, and partner across teams to deliver fast, efficient inference at scale for customers.

Qualifications

• Bachelor's, Master’s, or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
• Experience managing engineers, including hiring, mentoring, giving feedback and running performance reviews.
• Experience leading or closely supporting GPU optimization teams in training, inference or recommendation systems.
• Strong technical depth in GPU workloads, with a solid understanding of GPU architecture and performance tradeoffs.
• Familiarity with ML libraries such as PyTorch, TensorRT or TensorRT-LLM.
• A track record of driving roadmaps and shipping complex technical projects with a team.
• Clear written and verbal communication, including the ability to align stakeholders across teams.

Responsibilities

• Lead, mentor and grow a team of inference performance engineers through regular 1:1s, clear feedback, career development and performance reviews.
• Hire top GPU and inference engineering talent, and build a strong, collaborative team culture as the runtime team scales.
• Own the technical roadmap and execution for runtime performance work, balancing customer needs, new model launches and long-term platform investments.
• Stay close to the technical work. Review designs, guide profiling and optimization efforts, and help the team reason from first principles about where time and memory go.
• Drive the productionization of inference techniques such as quantization, speculative decoding, KV-cache reuse, chunked prefill and custom scheduling.
• Turn performance wins into measurable outcomes: tokens per GPU-hour, utilization, latency and cost.
• Help the team bring up and tune new model architectures on new hardware quickly, often in the same week they're released.
• Partner with Infrastructure, Inference Platform, Kernels, Model APIs and customer-facing teams to set priorities, coordinate launches and ship wins.
• Set high standards for engineering quality, benchmarking, operational excellence and incident response.

Skills

GPU performance

Team leadership

Hiring

Profiling

CUDA

Education

Bachelor's/Master's/Ph.D. in CS/Engineering

Tools

PyTorch

TensorRT

TensorRT-LLM

vLLM

CUDA

Triton

CUTLASS

Job description

ABOUT BASETEN

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.

THE ROLE

We're looking for an Engineering Manager to lead part of our Inference Performance team. This team makes the world's most demanding AI workloads run faster and more efficiently on GPUs. You'll manage and grow a team of inference performance engineers working across the inference engine and runtime: kernels, scheduling, batching, KV-cache management, speculative decoding and prefill/decode disaggregation. This is a hands-on technical leadership role. You'll set direction, unblock hard problems and earn the team's trust by going deep on GPU performance, while also hiring, developing and supporting the people doing the work. Your team's output directly affects how fast our customers' models run and how efficiently we serve them. The team is scaling quickly, so you'll help shape how it is structured as it grows.

EXAMPLE INITIATIVES

Your team will work on these types of projects as part of our Inference Runtime team:

• Agentic inference optimization: 50-90% faster engines

• Agentic Kernels in Production

• Live draft model training for speculative decoding

• The Baseten Inference Stack

RESPONSIBILITIES

• Lead, mentor and grow a team of inference performance engineers through regular 1:1s, clear feedback, career development and performance reviews.

• Hire top GPU and inference engineering talent, and build a strong, collaborative team culture as the runtime team scales.

• Own the technical roadmap and execution for runtime performance work, balancing customer needs, new model launches and long-term platform investments.

• Stay close to the technical work. Review designs, guide profiling and optimization efforts, and help the team reason from first principles about where time and memory go.

• Drive the productionization of inference techniques such as quantization, speculative decoding, KV-cache reuse, chunked prefill and custom scheduling.

• Turn performance wins into measurable outcomes: tokens per GPU-hour, utilization, latency and cost.

• Help the team bring up and tune new model architectures on new hardware quickly, often in the same week they're released.

• Partner with Infrastructure, Inference Platform, Kernels, Model APIs and customer-facing teams to set priorities, coordinate launches and ship wins.

• Set high standards for engineering quality, benchmarking, operational excellence and incident response.

REQUIREMENTS

• Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or a related field.

• Experience managing engineers, including hiring, mentoring, giving feedback and running performance reviews.

• Experience leading or closely supporting GPU optimization teams in training, inference or recommendation systems.

• Strong technical depth in GPU workloads, with a solid understanding of GPU architecture and performance tradeoffs.

• Familiarity with ML libraries such as PyTorch, TensorRT or TensorRT-LLM.

• A track record of driving roadmaps and shipping complex technical projects with a team.

• Clear written and verbal communication, including the ability to align stakeholders across teams.

NICE TO HAVE

• Familiarity with inference engines such as vLLM, SGLang or TensorRT-LLM.

• Experience with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching) in production.

• Experience with GPU kernels (CUDA, Triton, CUTLASS, or similar).

• Experience scaling a team through rapid growth at a startup.

• A background as a hands-on performance or systems engineer before moving into management.

BENEFITS

• Competitive compensation, including meaningful equity

• (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents

• Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)

• Paid parental leave

• Fertility and family-building stipend through Carrot

• (U.S. only) Company-facilitated 401(k)

• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).